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import torch
from torch import nn
from .transformer import (
PositionalEncoding,
TransformerDecoderLayer,
TransformerDecoder,
)
from core.networks.dynamic_fc_decoder import DynamicFCDecoderLayer, DynamicFCDecoder
from core.utils import _reset_parameters
def get_decoder_network(
network_type,
d_model,
nhead,
dim_feedforward,
dropout,
activation,
normalize_before,
num_decoder_layers,
return_intermediate_dec,
dynamic_K,
dynamic_ratio,
):
decoder = None
if network_type == "TransformerDecoder":
decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout, activation, normalize_before)
norm = nn.LayerNorm(d_model)
decoder = TransformerDecoder(
decoder_layer,
num_decoder_layers,
norm,
return_intermediate_dec,
)
elif network_type == "DynamicFCDecoder":
d_style = d_model
decoder_layer = DynamicFCDecoderLayer(
d_model,
nhead,
d_style,
dynamic_K,
dynamic_ratio,
dim_feedforward,
dropout,
activation,
normalize_before,
)
norm = nn.LayerNorm(d_model)
decoder = DynamicFCDecoder(decoder_layer, num_decoder_layers, norm, return_intermediate_dec)
else:
raise ValueError(f"Invalid network_type {network_type}")
return decoder
class DisentangleDecoder(nn.Module):
def __init__(
self,
d_model=512,
nhead=8,
num_decoder_layers=3,
dim_feedforward=2048,
dropout=0.1,
activation="relu",
normalize_before=False,
return_intermediate_dec=False,
pos_embed_len=80,
upper_face3d_indices=tuple(list(range(19)) + list(range(46, 51))),
lower_face3d_indices=tuple(range(19, 46)),
network_type="None",
dynamic_K=None,
dynamic_ratio=None,
**_,
) -> None:
super().__init__()
self.upper_face3d_indices = upper_face3d_indices
self.lower_face3d_indices = lower_face3d_indices
# upper_decoder_layer = TransformerDecoderLayer(
# d_model, nhead, dim_feedforward, dropout, activation, normalize_before
# )
# upper_decoder_norm = nn.LayerNorm(d_model)
# self.upper_decoder = TransformerDecoder(
# upper_decoder_layer,
# num_decoder_layers,
# upper_decoder_norm,
# return_intermediate=return_intermediate_dec,
# )
self.upper_decoder = get_decoder_network(
network_type,
d_model,
nhead,
dim_feedforward,
dropout,
activation,
normalize_before,
num_decoder_layers,
return_intermediate_dec,
dynamic_K,
dynamic_ratio,
)
_reset_parameters(self.upper_decoder)
# lower_decoder_layer = TransformerDecoderLayer(
# d_model, nhead, dim_feedforward, dropout, activation, normalize_before
# )
# lower_decoder_norm = nn.LayerNorm(d_model)
# self.lower_decoder = TransformerDecoder(
# lower_decoder_layer,
# num_decoder_layers,
# lower_decoder_norm,
# return_intermediate=return_intermediate_dec,
# )
self.lower_decoder = get_decoder_network(
network_type,
d_model,
nhead,
dim_feedforward,
dropout,
activation,
normalize_before,
num_decoder_layers,
return_intermediate_dec,
dynamic_K,
dynamic_ratio,
)
_reset_parameters(self.lower_decoder)
self.pos_embed = PositionalEncoding(d_model, pos_embed_len)
tail_hidden_dim = d_model // 2
self.upper_tail_fc = nn.Sequential(
nn.Linear(d_model, tail_hidden_dim),
nn.ReLU(),
nn.Linear(tail_hidden_dim, tail_hidden_dim),
nn.ReLU(),
nn.Linear(tail_hidden_dim, len(upper_face3d_indices)),
)
self.lower_tail_fc = nn.Sequential(
nn.Linear(d_model, tail_hidden_dim),
nn.ReLU(),
nn.Linear(tail_hidden_dim, tail_hidden_dim),
nn.ReLU(),
nn.Linear(tail_hidden_dim, len(lower_face3d_indices)),
)
def forward(self, content, style_code):
"""
Args:
content (_type_): (B, num_frames, window, C_dmodel)
style_code (_type_): (B, C_dmodel)
Returns:
face3d: (B, L_clip, C_3dmm)
"""
B, N, W, C = content.shape
style = style_code.reshape(B, 1, 1, C).expand(B, N, W, C)
style = style.permute(2, 0, 1, 3).reshape(W, B * N, C)
# (W, B*N, C)
content = content.permute(2, 0, 1, 3).reshape(W, B * N, C)
# (W, B*N, C)
tgt = torch.zeros_like(style)
pos_embed = self.pos_embed(W)
pos_embed = pos_embed.permute(1, 0, 2)
upper_face3d_feat = self.upper_decoder(tgt, content, pos=pos_embed, query_pos=style)[0]
# (W, B*N, C)
upper_face3d_feat = upper_face3d_feat.permute(1, 0, 2).reshape(B, N, W, C)[:, :, W // 2, :]
# (B, N, C)
upper_face3d = self.upper_tail_fc(upper_face3d_feat)
# (B, N, C_exp)
lower_face3d_feat = self.lower_decoder(tgt, content, pos=pos_embed, query_pos=style)[0]
lower_face3d_feat = lower_face3d_feat.permute(1, 0, 2).reshape(B, N, W, C)[:, :, W // 2, :]
lower_face3d = self.lower_tail_fc(lower_face3d_feat)
C_exp = len(self.upper_face3d_indices) + len(self.lower_face3d_indices)
face3d = torch.zeros(B, N, C_exp).to(upper_face3d)
face3d[:, :, self.upper_face3d_indices] = upper_face3d
face3d[:, :, self.lower_face3d_indices] = lower_face3d
return face3d
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